Polynomial Smoothing of Time Series With Additive Step Discontinuities

Selesnick, Ivan; Arnold, Stephen; Dantham, Venkata Ramanaiah

doi:10.1109/tsp.2012.2214219

Cited by 35 publications

(35 citation statements)

References 32 publications

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“…Data were interpolated using a polynomial regression model fitted to each patient's migration data received by EBRA-FCA to calculate the migration rate at equal times as described previously [3,27,28]. We used a polynomialbased analysis to account for the fact that the stem subsides somewhat during the first weeks or months after implantation, but that later migration usually slows down and Fig.…”

Section: Discussionmentioning

confidence: 99%

Early Migration Predicts Aseptic Loosening of Cementless Femoral Stems: A Long-term Study

Streit

Haeussler

Brückner

et al. 2016

Clinical Orthopaedics &Amp; Related Research

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Background Excessive early migration of cemented stems and cups after THA has been associated with poor long-term survival and allows predictable evaluation of implant performance. However, there are few data regarding the relationship between early migration and aseptic loosening of cementless femoral components, and whether early migration might predict late failure has not been evaluated, to our knowledge. Einzel-Bild-Röntgen-Analyse-femoral component analysis (EBRA-FCA) is a validated technique to accurately measure axial femoral stem migration without the need for tantalum markers, can be performed retrospectively, and may be a suitable tool to identify poor performing implants before their widespread use. Questions/purposes We asked: (1) Is axial migration within the first 24 months as assessed by EBRA-FCA greater among cementless stems that develop aseptic loosening than those that remain well fixed through the second decade; (2) what is the diagnostic performance of implant migration at 24 months postoperatively to predict later aseptic loosening of these components; and (3) how does long-term stem survivorship compare between groups with high and low early migration? Methods We evaluated early axial stem migration in 158 cementless THAs using EBRA-FCA. The EBRA-FCA measurements were performed during the first week postoperatively (baseline measurement) and at regular followups of 3, 6, and 12 months postoperatively and annually thereafter. The mean duration of followup was 21 years (range, 18-24 years). The stems studied represented 45% (158 of 354) of the cementless THAs performed during that time, and cementless THAs represented 34% (354 of 1038) of the THA practice during that period. No patient enrolled in this study was lost to followup. Multivariate survivorship analysis using Cox's regression model The institution of one or more of the authors (MRS) has received, during the study period, funding from the noncommercial research fund of Deutsche Arthrose-Hilfe e.V (Frankfurt, Germany). The institution of one or more of the authors (TG) has received, during the study period, funding from Biomet Europe (Berlin, Germany) and from Depuy Orthopädie GmbH (Kirkel, Germany). One of the authors certifies that he (TG), or a member of his immediate family, has or may receive payments or benefits, during the study period, an amount of less than USD 10,000 from Pfizer Pharma GmbH (Karlsruhe, Germany), and an amount of less than USD 10,000 from MSD Sharp&Dohme (Haar, Germany was performed with an endpoint of aseptic loosening of the femoral component. Loosening was defined according to the criteria described by Engh et al. and assessed by two independent observers. Patients with a diagnosis of prosthetic joint infection were excluded. Receiver operating characteristic (ROC) curve analysis was used to evaluate diagnostic performance of axial stem migration 1, 2, 3, and 4 years postoperatively as a predictor of aseptic loosening. Survivorship of hips with high (C 2.7 mm) and low (\ 2.7 mm) migration was co...

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Section: Discussionmentioning

confidence: 99%

Early Migration Predicts Aseptic Loosening of Cementless Femoral Stems: A Long-term Study

Streit

Haeussler

Brückner

et al. 2016

Clinical Orthopaedics &Amp; Related Research

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“…This motivates the development of semidefinite algorithms to solve (33) where H is not explicitly available, but for which multiplications by H and H T are fast (this is not addressed in this paper). Nevertheless, for 1D problems of 'medium'-size (arising for example in biomedical applications [61]), (33) is readily solved via existing software. In case (33) is too computationally demanding, then the suboptimal choice R = α min I can be used as in GNC [6], [50].…”

Section: B Diagonal Lower Bound Matrix Computationmentioning

confidence: 99%

“…Specific motivating applications include nano-particle detection for bio-sensing and near infrared spectroscopic time series imaging [61], [62]. This paper explores the use of non-convex penalty functions φ n , under the constraint that the total cost function F is convex and therefore reliably minimized.…”

Section: Introductionmentioning

confidence: 99%

Sparse Signal Estimation by Maximally Sparse Convex Optimization

Selesnick¹,

Bayram

2014

IEEE Trans. Signal Process.

162

125

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Abstract-This paper addresses the problem of sparsity penalized least squares for applications in sparse signal processing, e.g. sparse deconvolution. This paper aims to induce sparsity more strongly than L1 norm regularization, while avoiding nonconvex optimization. For this purpose, this paper describes the design and use of non-convex penalty functions (regularizers) constrained so as to ensure the convexity of the total cost function, F, to be minimized. The method is based on parametric penalty functions, the parameters of which are constrained to ensure convexity of F. It is shown that optimal parameters can be obtained by semidefinite programming (SDP). This maximally sparse convex (MSC) approach yields maximally non-convex sparsity-inducing penalty functions constrained such that the total cost function, F, is convex. It is demonstrated that iterative MSC (IMSC) can yield solutions substantially more sparse than the standard convex sparsity-inducing approach, i.e., L1 norm minimization.

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“…The problem addressed in this paper is closely related to the problem addressed in [70], [71]; however, the new approach described here has several advantages over the method described there. While [71] uses least squares polynomial approximation on overlapping blocks for signal smoothing, the new approach uses LTI filtering.…”

Section: A Related Workmentioning

confidence: 99%

“…While [71] uses least squares polynomial approximation on overlapping blocks for signal smoothing, the new approach uses LTI filtering. As a consequence, the new approach results in a time-invariant signal processing algorithm, in contrast to the approach of [71].…”

Section: A Related Workmentioning

confidence: 99%

Simultaneous Low-Pass Filtering and Total Variation Denoising

Selesnick¹,

Graber

Pfeil

et al. 2014

IEEE Trans. Signal Process.

131

105

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Abstract-This paper seeks to combine linear time-invariant (LTI) filtering and sparsity-based denoising in a principled way in order to effectively filter (denoise) a wider class of signals. LTI filtering is most suitable for signals restricted to a known frequency band, while sparsity-based denoising is suitable for signals admitting a sparse representation with respect to a known transform. However, some signals cannot be accurately categorized as either band-limited or sparse. This paper addresses the problem of filtering noisy data for the particular case where the underlying signal comprises a low-frequency component and a sparse or sparse-derivative component. A convex optimization approach is presented and two algorithms derived: one based on majorization-minimization (MM), and the other based on the alternating direction method of multipliers (ADMM). It is shown that a particular choice of discrete-time filter, namely zero-phase noncausal recursive filters for finite-length data formulated in terms of banded matrices, makes the algorithms effective and computationally efficient. The efficiency stems from the use of fast algorithms for solving banded systems of linear equations. The method is illustrated using data from a physiological-measurement technique (i.e., near infrared spectroscopic time series imaging) that in many cases yields data that is well-approximated as the sum of low-frequency, sparse or sparse-derivative, and noise components.

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Polynomial Smoothing of Time Series With Additive Step Discontinuities

Cited by 35 publications

References 32 publications

Early Migration Predicts Aseptic Loosening of Cementless Femoral Stems: A Long-term Study

Early Migration Predicts Aseptic Loosening of Cementless Femoral Stems: A Long-term Study

Sparse Signal Estimation by Maximally Sparse Convex Optimization

Simultaneous Low-Pass Filtering and Total Variation Denoising

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